Tootfinder

Two updates to old posts, both from social media bits:
1. https://adrianroselli.com/2023/09/the-children-are-driving-the-bus.html#Update01 adds this mini-thread for Bootlicker.js:

The Children Are Driving the Bus
The Vercel CEO over on the hell-site tweets: v0.dev produces the kind of production-grade code that we'd want to ship in our own @vercel products. That was the bar we set for ourselves. At the moment it can output HTML with @tailwindcss and React w/ @shadcn UI. pic.twitter.com/hWLzpmyaG2 Guillermo Rauch…

Duplicating Deceit: Inauthentic Behavior Among Indian Misinformation Duplicators on X/Twitter
Ashfaq Ali Shafin, Bogdan Carbunar
https://arxiv.org/abs/2507.13636

Duplicating Deceit: Inauthentic Behavior Among Indian Misinformation Duplicators on X/Twitter
This paper investigates inauthentic duplication on social media, where multiple accounts share identical misinformation tweets. Leveraging a dataset of misinformation verified by AltNews, an Indian fact-checking organization, we analyze over 12 million posts from 5,493 accounts known to have duplicated such content. Contrary to common assumptions that bots are primarily responsible for spreading false information, fewer than 1\% of these accounts exhibit bot-like behavior. We present TweeXster,…

Online Anti-sexist Speech: Identifying Resistance to Gender Bias in Political Discourse
Aditi Dutta, Susan Banducci
https://arxiv.org/abs/2508.11434 https://

Online Anti-sexist Speech: Identifying Resistance to Gender Bias in Political Discourse
Anti-sexist speech, i.e., public expressions that challenge or resist gendered abuse and sexism, plays a vital role in shaping democratic debate online. Yet automated content moderation systems, increasingly powered by large language models (LLMs), may struggle to distinguish such resistance from the sexism it opposes. This study examines how five LLMs classify sexist, anti-sexist, and neutral political tweets from the UK, focusing on high-salience trigger events involving female Members of Par…

[Carl Sagan voice]
If you wish to make an apple pie from scratch, you must first stop reposting screenshots of tweets from fascist government agencies.

At least 2/3 of Americans aren’t daily coffee drinkers. ☕️
Wait, they are.
Well, at least hundreds of millions of American coffee drinkers don’t drink an average of 3 cups a day.
Wait, they do.
Folks, these are #TrumpTariffs, taking money from the American people.
via @…

What is happening here? F-35s for fighting against drugs traffickers is very strange. An attack against Venezuela would be a good distraction for Trump from everything that is going wrong elsewhere.
https://www.twz.com/air/10-f-35s-deploying

Language bubbles in online social networks
Alessandro Bellina, Donald Ruggiero Lo Sardo, Emanuele Brugnoli, Fabio Saracco, Pietro Gravino, Vittorio Loreto, Gabriele Di Bona
https://arxiv.org/abs/2507.13068

Language bubbles in online social networks
Social media platforms have become essential spaces for public discourse. While political polarisation and limited communication across different groups are widely acknowledged, the connection between social network fragmentation and the language features and quality used by various communities has received insufficient attention. This study aims to fill this gap by examining the social structure and linguistic richness of the Italian debate on Twitter/X. We analyse tweets and retweets from Ita…

Analysis of Propaganda in Tweets From Politically Biased Sources
Vivek Sharma, Mohammad Mahdi Shokri, Sarah Ita Levitan, Elena Filatova, Shweta Jain
https://arxiv.org/abs/2507.08169

Analysis of Propaganda in Tweets From Politically Biased Sources
News outlets are well known to have political associations, and many national outlets cultivate political biases to cater to different audiences. Journalists working for these news outlets have a big impact on the stories they cover. In this work, we present a methodology to analyze the role of journalists, affiliated with popular news outlets, in propagating their bias using some form of propaganda-like language. We introduce JMBX(Journalist Media Bias on X), a systematically collected and ann…

Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages
Andrea Nasuto, Stefano Maria Iacus, Francisco Rowe, Devika Jain
https://arxiv.org/abs/2508.06435

Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages
Large language models (LLMs) are transforming social-science research by enabling scalable, precise analysis. Their adaptability raises the question of whether knowledge acquired through fine-tuning in a few languages can transfer to unseen languages that only appeared during pre-training. To examine this, we fine-tune lightweight LLaMA 3.2-3B models on monolingual, bilingual, or multilingual data sets to classify immigration-related tweets from X/Twitter across 13 languages, a domain character…

OSINT or BULLSHINT? Exploring Open-Source Intelligence tweets about the Russo-Ukrainian War
Johannes Niu, Mila Stillman, Anna Kruspe
https://arxiv.org/abs/2508.03599 https://

OSINT or BULLSHINT? Exploring Open-Source Intelligence tweets about the Russo-Ukrainian War
This paper examines the role of Open Source Intelligence (OSINT) on Twitter regarding the Russo-Ukrainian war, distinguishing between genuine OSINT and deceptive misinformation efforts, termed "BULLSHINT." Utilizing a dataset spanning from January 2022 to July 2023, we analyze nearly 2 million tweets from approximately 1,040 users involved in discussing real-time military engagements, strategic analyses, and misinformation related to the conflict. Using sentiment analysis, partisanship detectio…